Systems, methods, and apparatuses for distributing computational resources over a network of luminaires

ABSTRACT

The described embodiments relate to systems, methods, and apparatus for employing a network (410) of smart luminaires (402, 440) to perform tasks typically reserved for remote servers. The network of smart luminaires can include lighting elements for illuminating an area, as well as a computer system for processing and transmitting data. Various computational tasks can be parsed and delegated to certain smart luminaires in the network (410) in order to optimize the use of each smart luminaire in the network. Computational tasks can originate at the smart luminaires or be delegated to the smart luminaires by another device. Additionally, data that is processed by the smart luminaires can be transmitted to other devices, thereby allowing other devices to leverage the computing power of a nearby network of smart luminaires.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§ 371 of International Application No. PCT/EP2017/073614, filed on Sep.19, 2017 which claims the benefit of U.S. Provisional Patent ApplicationNo. 62/399,327, filed on Sep. 23, 2016, and European Patent ApplicationNo. 16202267.7. These applications are hereby incorporated by referenceherein.

TECHNICAL FIELD

The present invention is directed generally to systems, methods, andapparatuses for optimizing network resources. More particularly, variousinventive methods and apparatuses disclosed herein relate to employingnetwork of luminaires to perform computational tasks.

BACKGROUND

Digital lighting technologies, i.e., illumination based on semiconductorlight sources, such as light-emitting diodes (LEDs), offer a viablealternative to traditional fluorescent, HID, and incandescent lamps.Functional advantages and benefits of LEDs include high energyconversion and optical efficiency, durability, lower operating costs,and many others. Recent advances in LED technology have providedefficient and robust full-spectrum lighting sources that enable avariety of lighting effects in many applications. Some of the fixturesembodying these sources feature a lighting module, including one or moreLEDs capable of producing different colors, e.g., red, green, and blue,as well as a processor for independently controlling the output of theLEDs in order to generate a variety of colors and color-changinglighting effects, for example, as discussed in detail in U.S. Pat. Nos.6,016,038 and 6,211,626, incorporated herein by reference.

Certain technologies offer the ability to off load computational tasksto a remote server in order to conserve available resources at localdevices. Although remote servers can offer additional processing power,the task of offloading can consume bandwidth and cause delays for othernetwork traffic. Furthermore, remote servers that manage a variety ofaccounts can have longer wait times for queries to be received.Therefore, any resolution to a query or processing task may be furtherdelayed by offloading the task to a remote server.

US 20150042240 A1 relates to a lighting system utilizes intelligentsystem elements, such as lighting devices, user interfaces for lightingcontrol or the like and possibly sensors. The system also has a datacommunication network. Some number of the intelligent lighting systemelements, including at least two of the lighting devices, also supportcommunication with non-lighting-system devices at the premises. Eachsuch element has a communication interface system configured to providea data communication link for use by non-lighting-system devices at thepremises in proximity to the respective intelligent system element.Also, in such an element, the processor is configured to controlcommunications via the communication interface system so as to provideaccess to the data network and through the data network to the wide areanetwork outside the premises for non-lighting related communications ofthe non-lighting-system devices.

US 20140035482 A1 relates to a system of network-connected lightingdevices also offers a distributed processing function that utilizesprocessor and/or memory resources if/when available in some or all ofthe lighting devices. In the examples, a resource manager receives a jobfor distributed processing using shared available resources. Theresource manager identifies lighting devices having resources of theprocessors and/or the memories available for the distributed processingfunction. The resource manager distributes tasks and/or data of thereceived job through a communications network to identified lightingdevices, for distributed processing. The resource manager also receivesresults of distributed processing for the received job, from theidentified lighting devices through the communications network. Thereceived results are processed to produce a composite result for aresponse to the received job.

SUMMARY

The present disclosure is directed to systems, methods, and apparatusfor leveraging the processing power of a network of smart luminairedevices to perform computationally intensive tasks without the need tooffload the tasks to remote servers. For example, a network of luminairedevices can perform imaging processing tasks on images taken in an areailluminated by the luminaire devices by parsing the image into segmentsand distributing the segmented images among the individual luminairedevices for processing.

Generally, in one aspect, a method is set forth for performing acomputational task using a network of lighting apparatuses. The methodcan be performed by a lighting apparatus in the network of lightingapparatuses, or any device or apparatus discussed herein. The method caninclude the steps of: receiving task schedules from lighting apparatusesin the network of lighting apparatuses; determining, based on the taskschedules, an amount of computational resources available from thelighting apparatuses; and determining an occupancy pattern of a roomilluminated by at least one lighting apparatus in the network oflighting apparatuses. The method can also include the step of:identifying a subgroup of lighting apparatuses in the network oflighting apparatuses that have available computational resources,wherein identifying the subgroup of lighting apparatuses is at leastpartially based on the occupancy pattern of the room. The steps can alsoinclude distributing the computational task to the subgroup of lightingapparatuses in the network of lighting apparatuses. The occupancypattern can be determined using one or more cameras installed in one ormore lighting apparatuses in the network of lighting apparatuses. Themethod can also include a step of receiving energy usage data from thelighting apparatuses in the network of lighting apparatuses, whereindistributing the computational task to the subgroup is at leastpartially based on the received energy usage data. The method canfurther include a step of determining a connectivity requirement fordistributing the computational task to the subgroup of lightingapparatuses in the network of lighting apparatuses. Additionally, themethod can include a step of parsing the computational task intosubtasks at least partially based on the connectivity requirement fordistributing the computational task, wherein distributing thecomputational task includes distributing the subtasks. The method canalso include steps of determining that a fault has occurred at asubgroup lighting apparatus; and redistributing a subtask from thesubgroup lighting apparatus to a different subgroup lighting apparatus.

In another aspect, a lighting apparatus is set forth. The lightingapparatus can include a lighting unit, and a wireless transceiverconfigured to connect to a wireless lighting network. The lightingapparatus can also include a sensor configured to transmit a signalbased on environmental conditions illuminated by the lighting unit, anda processor connected to the sensor, the wireless transceiver, and thelighting unit. The processor can be configured to (i) receive the signalfrom the sensor, (ii) parse the signal into segmented data fordistribution by the wireless transceiver over the wireless lightingnetwork, and (iii) receive, from an affiliated lighting apparatusconnected to the wireless lighting network, processed data based on thesegmented data distributed by the wireless transceiver. The sensor canbe a camera and the segmented data can be image data. The processor canbe further configured to control the lighting unit based on theprocessed data received from the affiliated lighting apparatus. Thelighting apparatus can further include a memory device that stores aprogram for analyzing program data received from the affiliated lightingapparatus, wherein the wireless transceiver is further configured totransmit the program data to the affiliated lighting apparatus. Thememory device can also store a network protocol for communicating withthe affiliated lighting apparatus over the wireless lighting network.The network protocol can be configured for wireless communications overa network consisting of multiple affiliated lighting apparatuses.

In yet another aspect, a non-transitory computer-readable medium is setforth as storing instructions that can be executed by one or moreprocessors of a computing device. The execution of the instructions cancause the computing device to perform steps that include: receiving,from a lighting apparatus in a network of connected lightingapparatuses, charge data that indicates a battery charge level of thelighting apparatus, and identifying, at least partially based on thecharge data received from the lighting apparatus, a subgroup of lightingapparatuses to perform a computational task. The steps can also includeparsing the computational task into subtasks to be performed byindividual lighting apparatuses, and transmitting, to the individuallighting apparatuses, data corresponding to the subtasks. The steps canfurther include determining that the battery charge level is within alow threshold stored by the computing device, wherein the lightingapparatus is excluded from the subgroup based on the battery chargelevel being within the low threshold. The steps can also include causingat least one lighting apparatus in the network of lighting apparatusesto share battery charge data with the lighting apparatus excluded fromthe subgroup. Additionally, the steps can include determining that thebattery charge level is within a high threshold stored by the computingdevice, wherein the lighting apparatus is included in the subgroup basedon the battery charge level being within the high threshold. The stepscan also include determining that the computational task is complete;receiving an updated battery charge level from the lighting apparatus;and determining an amount of charge consumed by the lighting apparatusas a result of performing the task. Furthermore, the steps can includecollecting environmental data using a sensor of the computing device;parsing the collected environmental data into subsets of collected data;and transmitting, to the individual lighting apparatuses, the subsets ofcollected data with the data corresponding to the subtasks. Thecomputational task includes determining whether motion has occurred inan environment illuminated by the network of connected lightingapparatuses.

As used herein for purposes of the present disclosure, the term “LED”should be understood to include any electroluminescent diode or othertype of carrier injection/junction-based system that is capable ofgenerating radiation in response to an electric signal. Thus, the termLED includes, but is not limited to, various semiconductor-basedstructures that emit light in response to current, light emittingpolymers, organic light emitting diodes (OLEDs), electroluminescentstrips, and the like. In particular, the term LED refers to lightemitting diodes of all types (including semi-conductor and organic lightemitting diodes) that may be configured to generate radiation in one ormore of the infrared spectrum, ultraviolet spectrum, and variousportions of the visible spectrum (generally including radiationwavelengths from approximately 400 nanometers to approximately 700nanometers). Some examples of LEDs include, but are not limited to,various types of infrared LEDs, ultraviolet LEDs, red LEDs, blue LEDs,green LEDs, yellow LEDs, amber LEDs, orange LEDs, and white LEDs(discussed further below). It also should be appreciated that LEDs maybe configured and/or controlled to generate radiation having variousbandwidths (e.g., full widths at half maximum, or FWHM) for a givenspectrum (e.g., narrow bandwidth, broad bandwidth), and a variety ofdominant wavelengths within a given general color categorization.

For example, one implementation of an LED configured to generateessentially white light (e.g., a white LED) may include a number of dieswhich respectively emit different spectra of electroluminescence that,in combination, mix to form essentially white light. In anotherimplementation, a white light LED may be associated with a phosphormaterial that converts electroluminescence having a first spectrum to adifferent second spectrum. In one example of this implementation,electroluminescence having a relatively short wavelength and narrowbandwidth spectrum “pumps” the phosphor material, which in turn radiateslonger wavelength radiation having a somewhat broader spectrum.

It should also be understood that the term LED does not limit thephysical and/or electrical package type of an LED. For example, asdiscussed above, an LED may refer to a single light emitting devicehaving multiple dies that are configured to respectively emit differentspectra of radiation (e.g., that may or may not be individuallycontrollable). Also, an LED may be associated with a phosphor that isconsidered as an integral part of the LED (e.g., some types of whiteLEDs). In general, the term LED may refer to packaged LEDs, non-packagedLEDs, surface mount LEDs, chip-on-board LEDs, T-package mount LEDs,radial package LEDs, power package LEDs, LEDs including some type ofencasement and/or optical element (e.g., a diffusing lens), etc.

The term “light source” should be understood to refer to any one or moreof a variety of radiation sources, including, but not limited to,LED-based sources (including one or more LEDs as defined above),incandescent sources (e.g., filament lamps, halogen lamps), HID, HPS,fluorescent sources, phosphorescent sources, high-intensity dischargesources (e.g., sodium vapor, mercury vapor, and metal halide lamps),lasers, other types of electroluminescent sources, pyro-luminescentsources (e.g., flames), candle-luminescent sources (e.g., gas mantles,carbon arc radiation sources), photo-luminescent sources (e.g., gaseousdischarge sources), cathode luminescent sources using electronicsatiation, galvano-luminescent sources, crystallo-luminescent sources,kine-luminescent sources, thermo-luminescent sources, triboluminescentsources, sonoluminescent sources, radioluminescent sources, andluminescent polymers.

A given light source may be configured to generate electromagneticradiation within the visible spectrum, outside the visible spectrum, ora combination of both. Hence, the terms “light” and “radiation” are usedinterchangeably herein. Additionally, a light source may include as anintegral component one or more filters (e.g., color filters), lenses, orother optical components. Also, it should be understood that lightsources may be configured for a variety of applications, including, butnot limited to, indication, display, and/or illumination. An“illumination source” is a light source that is particularly configuredto generate radiation having a sufficient intensity to effectivelyilluminate an interior or exterior space. In this context, “sufficientintensity” refers to sufficient radiant power in the visible spectrumgenerated in the space or environment (the unit “lumens” often isemployed to represent the total light output from a light source in alldirections, in terms of radiant power or “luminous flux”) to provideambient illumination (i.e., light that may be perceived indirectly andthat may be, for example, reflected off of one or more of a variety ofintervening surfaces before being perceived in whole or in part).

The term “spectrum” should be understood to refer to any one or morefrequencies (or wavelengths) of radiation produced by one or more lightsources. Accordingly, the term “spectrum” refers to frequencies (orwavelengths) not only in the visible range, but also frequencies (orwavelengths) in the infrared, ultraviolet, and other areas of theoverall electromagnetic spectrum. Also, a given spectrum may have arelatively narrow bandwidth (e.g., a FWHM having essentially fewfrequency or wavelength components) or a relatively wide bandwidth(several frequency or wavelength components having various relativestrengths). It should also be appreciated that a given spectrum may bethe result of a mixing of two or more other spectra (e.g., mixingradiation respectively emitted from multiple light sources).

For purposes of this disclosure, the term “color” is usedinterchangeably with the term “spectrum.” However, the term “color”generally is used to refer primarily to a property of radiation that isperceivable by an observer (although this usage is not intended to limitthe scope of this term). Accordingly, the terms “different colors”implicitly refer to multiple spectra having different wavelengthcomponents and/or bandwidths. It also should be appreciated that theterm “color” may be used in connection with both white and non-whitelight.

The term “color temperature” generally is used herein in connection withwhite light, although this usage is not intended to limit the scope ofthis term. Color temperature essentially refers to a particular colorcontent or shade (e.g., reddish, bluish) of white light. The colortemperature of a given radiation sample conventionally is characterizedaccording to the temperature in degrees Kelvin (K) of a black bodyradiator that radiates essentially the same spectrum as the radiationsample in question. Black body radiator color temperatures generallyfall within a range of approximately 700 degrees K (typically consideredthe first visible to the human eye) to over 10,000 degrees K; whitelight generally is perceived at color temperatures above 1500-2000degrees K.

Lower color temperatures generally indicate white light having a moresignificant red component or a “warmer feel,” while higher colortemperatures generally indicate white light having a more significantblue component or a “cooler feel.” By way of example, fire has a colortemperature of approximately 1,800 degrees K, a conventionalincandescent bulb has a color temperature of approximately 2848 degreesK, early morning daylight has a color temperature of approximately 3,000degrees K, and overcast midday skies have a color temperature ofapproximately 10,000 degrees K. A color image viewed under white lighthaving a color temperature of approximately 3,000 degree K has arelatively reddish tone, whereas the same color image viewed under whitelight having a color temperature of approximately 10,000 degrees K has arelatively bluish tone.

The term “lighting fixture” is used herein to refer to an implementationor arrangement of one or more lighting units in a particular formfactor, assembly, or package. The term “lighting unit” is used herein torefer to an apparatus including one or more light sources of same ordifferent types. A given lighting unit may have any one of a variety ofmounting arrangements for the light source(s), enclosure/housingarrangements and shapes, and/or electrical and mechanical connectionconfigurations. Additionally, a given lighting unit optionally may beassociated with (e.g., include, be coupled to and/or packaged togetherwith) various other components (e.g., control circuitry) relating to theoperation of the light source(s). An “LED-based lighting unit” refers toa lighting unit that includes one or more LED-based light sources asdiscussed above, alone or in combination with other non LED-based lightsources. A “multi-channel” lighting unit refers to an LED-based or nonLED-based lighting unit that includes at least two light sourcesconfigured to respectively generate different spectrums of radiation,wherein each different source spectrum may be referred to as a “channel”of the multi-channel lighting unit.

The term “controller” is used herein generally to describe variousapparatus relating to the operation of one or more light sources. Acontroller can be implemented in numerous ways (e.g., such as withdedicated hardware) to perform various functions discussed herein. A“processor” is one example of a controller, which employs one or moremicroprocessors that may be programmed using software (e.g., microcode)to perform various functions discussed herein. A controller may beimplemented with or without employing a processor, and also may beimplemented as a combination of dedicated hardware to perform somefunctions and a processor (e.g., one or more programmed microprocessorsand associated circuitry) to perform other functions. Examples ofcontroller components that may be employed in various embodiments of thepresent disclosure include, but are not limited to, conventionalmicroprocessors, application specific integrated circuits (ASICs), andfield-programmable gate arrays (FPGAs).

In various implementations, a processor or controller may be associatedwith one or more storage media (generically referred to herein as“memory,” e.g., volatile and non-volatile computer memory such as RAM,PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks,magnetic tape, etc.). In some implementations, the storage media may beencoded with one or more programs that, when executed on one or moreprocessors and/or controllers, perform at least some of the functionsdiscussed herein. Various storage media may be fixed within a processoror controller or may be transportable, such that the one or moreprograms stored thereon can be loaded into a processor or controller soas to implement various aspects of the present invention discussedherein. The terms “program” or “computer program” are used herein in ageneric sense to refer to any type of computer code (e.g., software ormicrocode) that can be employed to program one or more processors orcontrollers.

The term “addressable” is used herein to refer to a device (e.g., alight source in general, a lighting unit or fixture, a controller orprocessor associated with one or more light sources or lighting units,other non-lighting related devices, etc.) that is configured to receiveinformation (e.g., data) intended for multiple devices, includingitself, and to selectively respond to particular information intendedfor it. The term “addressable” often is used in connection with anetworked environment (or a “network,” discussed further below), inwhich multiple devices are coupled together via some communicationsmedium or media.

In one network implementation, one or more devices coupled to a networkmay serve as a controller for one or more other devices coupled to thenetwork (e.g., in a master/slave relationship). In anotherimplementation, a networked environment may include one or morededicated controllers that are configured to control one or more of thedevices coupled to the network. Generally, multiple devices coupled tothe network each may have access to data that is present on thecommunications medium or media; however, a given device may be“addressable” in that it is configured to selectively exchange data with(i.e., receive data from and/or transmit data to) the network, based,for example, on one or more particular identifiers (e.g., “addresses”)assigned to it.

The term “network” as used herein refers to any interconnection of twoor more devices (including controllers or processors) that facilitatesthe transport of information (e.g., for device control, data storage,data exchange, etc.) between any two or more devices and/or amongmultiple devices coupled to the network. As should be readilyappreciated, various implementations of networks suitable forinterconnecting multiple devices may include any of a variety of networktopologies and employ any of a variety of communication protocols.Additionally, in various networks according to the present disclosure,any one connection between two devices may represent a dedicatedconnection between the two systems, or alternatively a non-dedicatedconnection. In addition to carrying information intended for the twodevices, such a non-dedicated connection may carry information notnecessarily intended for either of the two devices (e.g., an opennetwork connection). Furthermore, it should be readily appreciated thatvarious networks of devices as discussed herein may employ one or morewireless, wire/cable, and/or fiber optic links to facilitate informationtransport throughout the network.

The term “user interface” as used herein refers to an interface betweena human user or operator and one or more devices that enablescommunication between the user and the device(s). Examples of userinterfaces that may be employed in various implementations of thepresent disclosure include, but are not limited to, switches,potentiometers, buttons, dials, sliders, a mouse, keyboard, keypad,various types of game controllers (e.g., joysticks), track balls,display screens, various types of graphical user interfaces (GUIs),touch screens, microphones and other types of sensors that may receivesome form of human-generated stimulus and generate a signal in responsethereto.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the invention.

FIG. 1 illustrates an example of a network of luminaires connectedwithin a building.

FIG. 2 illustrates an example of a network of luminaires connectedoutside of a building.

FIG. 3 provides a system diagram of a luminaire that can be included inany of the embodiments discussed herein.

FIG. 4A illustrates a luminaire network that includes a referenceluminaire with task data to be processed.

FIG. 4B illustrates a luminaire network where the task data has beendistributed to certain nodes of the luminaire network for processing.

FIG. 5 illustrates a method for distributing computational asks over anetwork of lighting apparatuses, according to some embodiments.

FIG. 6 illustrates a method for distributing and receiving segmenteddata to and from a network of lighting apparatuses for processing,according to some embodiments.

FIG. 7 illustrates a method for optimizing computational resources in anetwork of lighting devices, according to some embodiments.

FIG. 8 illustrates a method for delegating computational tasks tolighting apparatuses according to whether the lighting apparatuses arelocated in an occupied room.

DETAILED DESCRIPTION

The described embodiments relate to employing a network of smartluminaires to perform computational tasks. Resource allocation innetworks can typically rely on remote servers to perform computationaltasks. Although, such remote servers can provide temporary relief tolocal networks that have limited bandwidth for handling computationaltasks, the use of remote servers can cause latency at both the localnetwork and remote server. The embodiments set forth herein aimed atleveraging and optimizing the computational power and/ornon-computational resources (e.g., battery power) of local networkdevices. The local network devices can include smart luminaires, whichcan include lighting device that each have a lighting unit and acomputer system. The computer system can include network capabilitiesthat enable the lighting device to communicate wirelessly with otherlight lighting devices and share data with the other lighting devices.As a result, computationally intensive tasks can be parsed anddistributed across lighting devices in order to avoid employing, orreduce the reliance on, a remote server to accomplish the tasks.

A network of smart luminaires can handle a variety of tasks that canoriginate at the luminaires and/or at a remote device and be pushed tothe luminaires. For example, each luminaire can have a computing systemthat includes a sensor for perceiving environmental changes in areasilluminated by the luminaire. The sensor of a luminaire can collect dataabout the environment of the luminaire and the computing system cansegment the data into smaller sets of data for processing by otherluminaires in the network. For example, the sensor can be a camera thatis tasked with tracking the movement of people in a building, and thecomputer system of the luminaire can use the data from the camera toidentify and count the number of people in an area of the building at agiven time, and identify occupancy patterns for certain rooms in thebuilding. However, because the computational requirements for the imagescan be more than that of a single computer system of a luminaire, thecomputer system can segment the image load and transmit the segmentedimage data to other luminaires in the network in order to expedite theimage processing task.

In some embodiments, the sensor can be a remote device relative to anetwork of smart luminaires. For example, the sensor can collect dataabout an environment of the sensor and transmit the collected data tothe network of luminaires for processing. The network of luminaires canthen return processed data to the sensor. In this way, the sensor canavoid sending the collected data over public networks, such as theinternet, and improve processing times by leveraging the processingpower of local devices. For example, the network of smart luminaires canbe connected in an inventory warehouse, and the sensor can be setup toidentify shipments that are entering or leaving the inventory warehouse.Because shipment operations may require the sensor to constantly observethe inventory warehouse, shipment identification can be improved byoffloading sensor data analysis to the network of smart luminaires.Furthermore, illumination of the inventory warehouse can be adjustedaccording to the sensor data received by the network of luminaires,thereby potentially cutting energy costs for the inventory warehouse.

In some embodiments, the network of luminaires can be tasked withpre-processing data before the data is sent to a remote server. Eachluminaire of the network of luminaires can include a computer systemthat contains software for performing data processing tasks such ascompression, segmentation, and/or analysis. However, in someembodiments, such software can be pushed to the luminaires by separatedevice. Pre-processing tasks such as compression can be useful forreducing the amount of data required to be sent to a remote server forfurther processing. For example, a remote server can be tasked withperforming facial recognition on images provided to the remote serverfrom a local device that is also connected to the network of luminaires.However, before the local device sends the images to the remote server,the local device can send the images to the network of luminaires forcompression or other pre-processing (e.g., removing background imagedata and leaving foreground image data). Once pre-processing by thenetwork of luminaires has completed, the network of luminaires cantransmit the pre-processed image data back to the local device, and thelocal device can send the pre-processed image data to the remote server.By sending the pre-processed image data to the remote server instead ofthe non-processed image data, substantial bandwidth can be conservedthereby allowing other network processes to execute more efficiently.

In some embodiments, a subgroup of luminaires in a network of luminairescan be selected for computational tasks instead of the entire network ofluminaires, in order to more evenly spread the computational resourcesacross the network of luminaires. Each luminaire can include a computersystem with a memory that stores the specifications of the luminaire inorder that the luminaire can strategize how to best accomplish acomputational task. For example, a luminaire in the network ofluminaires can calculate a total amount of random access memory (RAM)available in a network of luminaires by summing the RAM sizes for eachluminaire in the network of luminaires. If a processing task involvescompressing an image, then depending on the size of the image, theluminaire can calculate the number of luminaires to task withcompressing the image. Furthermore, a luminaire can store specificationsregarding the bandwidth of the network of luminaires and determine pathsof least resistance or least network traffic for sending and receivingdata over the network of luminaires. For example, a luminaire can storea location of each luminaire on the network of luminaires and determinethe computational resources involved in sending data to the variousluminaires in the network of luminaires. In this way, the luminaire canavoid sending smaller computational tasks to more distant luminairesbecause such transmissions would consume computational resources ofother luminaires in the network along the way. In some embodiments, thenetwork of luminaires can be powered by a battery source, thereforereducing computational resources can also conserve charge for thebattery. Furthermore, each luminaire can calculate an estimate of powercost for a luminaire to transmit and complete a computational task, andtherefore avoid transmitting computational tasks to too many luminairesat a time in order to conserver power. By allowing the luminaires tocalculate the available computational and non-computational resources toperform a task, the need for middleware is eliminated. This allows fortasks such as computing available network battery power to be performedexclusively within the network of luminaires in order to avoid wastingresources on sending and receiving data to and from external devices.

Referring to FIG. 1, a diagram 100 is illustrated to provide an exampleof a network of luminaires 102 in a building 106 that includes multiplefloors 104. The network of luminaires 102 can be included on multipledifferent floors 104 of the building 106. Additionally, each luminaire102 can be capable of communicating directly or indirectly with everyother luminaire 102 in order that the luminaires can share data overmultiple areas and floors 104 of the building 106. Each luminaire 102can be a smart luminaire, and therefore have both a lighting unit and acomputer system (i.e., a controller) for performing computational tasks.The network of luminaires 102 can collect data and/or perform processingtasks for improving the operations that occur in and out of the building106. For example, the building 106 can be an office building and thelighting of the office building can be made dynamic according toidentified occupancy patterns of persons in rooms within the officebuilding. The occupancy patterns of persons within the building can bedetermined or predicted by the network of luminaires 102. In someembodiments, the building 106 can be an inventory building and thenetwork of luminaires 102 can be used to track inventory, without theneed for a remote server. Such computational tasks can be distributedover the network of luminaires 102 in order to more efficiently use eachluminaire 102 in the network of luminaires 102, as discussed herein.

FIG. 2 illustrates a diagram 200 that provides an example of a networkof luminaires 202 outside of a building 206. The network of luminaires202 can be incorporated into street lights that extend over a street 204outside of the building 206. The network of luminaires 202 can sendand/or receive data from computers within the building 206 or outsidethe building 206. For example, the network of luminaires 202 can betasked with tracking environmental changes outside of the building 206and communicate data about the environmental changes to computers withinthe building 206. Such environmental changes can include detectingpollution, counting persons outside the building 206, detecting trafficconditions outside the building 206, detecting velocity of objects orpersons outside, and/or detecting weather conditions outside thebuilding 206. Data related to the environmental changes can becompressed by the network of luminaires 202 before the data is sent tothe building. The data can be segmented and distributed to the otherluminaires 202 on the street 204 for further processing, and thereaftertransmitted back to the luminaire 202 that is closest to the building206. The luminaire 202 closest to the building 206 can then send thedata to the computer in the building 206.

In some embodiments, the network of luminaires 202 can communicate withpersonal devices of persons walking outside near the network ofluminaires 202. The personal devices can employ the network ofluminaires 202 with computational tasks in order to conserve energyand/or bandwidth of the personal devices. Such computational tasks caninclude compressing data before the data is sent over a long-termevolution (LTE) network. In some embodiments, a person's device cancommunicate with another person's device via the network of luminaires202. For example, a first person with a first device in building 206 cantypically use the internet or a cellular network to communicate with asecond device of a second person in a neighboring building 208. However,in order to reduce amount of bandwidth used in the building 206 Wi-Fi orcellular network, the first device and the second device can connect tothe network of luminaires 202 and communicate with each other over thenetwork of luminaires 202.

FIG. 3 provides a system diagram 300 of a luminaire 318 that can beincluded in any of the embodiments discussed herein. The luminaire 318can include a lighting unit 316, which can be any light source suitablefor providing a light output. The luminaire 318 can also include acomputer system 302. The computer system 302 can include a processingunit 304, which can include a central processing unit and/or a graphicsprocessing unit for performing computational tasks on data received atthe luminaire 318. The computer system 302 can also include a memoryunit 306. The memory unit 306 can include one or more memory devices,including, but not limited to, random access memory (RAM) and/or readonly memory (ROM). The memory unit 306 can store various data about theluminaire 318 as well as data related to any network that the luminaire318 is connected. For example, the luminaire 318 can store networkprotocols for communicating with other luminaires 318 over a network,specifications related processing speeds and memory sizes for differentluminaires 318 on the network, task schedules for different luminaries318 on the network, bandwidth data for the network, and/or any otherdata suitable for improving the efficiency of the network.

The luminaire 318 can also include a wireless transceiver 308 forsending and receiving signals to and from other luminaires 318 on anetwork, or other devices. The wireless transceiver 308 can include oneor more types of network interfaces for communicating with differenttypes of devices. For example, the wireless transceiver 308 can includea near-field transceiver for communicating with other near-fielddevices. The wireless transceiver 308 can also include an extended rangetransceiver that is capable of communicating over a Wi-Fi or long termevolution (LTE) network. The wireless transceiver 308 can also include awireless power transceiver for sending and receiving wireless power toand from other luminaires 318 respectively. Additionally, the luminaire318 can include a power management unit 310 for sending and receivingpower. The power management unit 310 can receive power from a powersource such as a battery or utility power supply and convert thereceived power for use by the lighting unit 316, computing system 302,and any other luminaires that can be connected to the luminaire 318. Thepower management unit 310 can also convert the received power forsending to other luminaires 318 in a network of luminaires 318.

The computing system 302 can also include input/output (I/O) ports 312for connecting with other devices. For example, the I/O ports 312 canconnect with optional sensors 314. The sensors 314 can include any typeof sensor including, but not limited to, image, sound, chemical,electric, air, mechanical, force, pressure, heat, proximity, and/or anyother type of sensor suitable for communicating signals in response toenvironmental changes. In this way, the luminaires 318 that include, orare connected to, sensors 314 can perform computational tasks that canimprove the environment in which the luminaires 318 are exposed.

FIG. 4A illustrates a diagram 400 of a luminaire network 410 thatincludes a luminaire 402 with task data 406 for processing. Theluminaire network 410 can include multiple interconnected nodes 430,where each node 430 represents a luminaire, such as the luminaires 102illustrated in FIG. 1 or luminaires 202 of FIG. 2. Each node 430 can beconnected through a wired and/or wireless connection 432 over whichdata, power, and/or any other signal can be transmitted. The luminaire402 can include a memory unit 404, which can store task data 406 andresource data 408. The task data 406 can originate at the luminaire 402or be transmitted to the luminaire 402 from an external source. Forexample, the task data 406 can be at least partially derived from asensor that is part of the luminaire 402 or remote from the luminaire402. The task data 406 can include segmented image data that has beensegmented by a processor of the luminaire 402 so that the segmentedimage data can be distributed among other nodes 430 in the luminairenetwork 410 for additional processing.

The resource data 408 stored in the memory unit 404 of the luminaire 402corresponds to computational resource data associated with the luminairenetwork 410. The resource data 408 can include a mapping of the nodes430 in the luminaire network 410. The mapping can allow the luminaire402 to make decisions about where to allocate certain computationaltasks in the luminaire network 410 while minimizing the reduction inbandwidth by such allocations. For example, bandwidth can be moreefficiently used by selecting nodes 430 that share the leastconnectivity requirement (i.e., number of connections 432) between thenodes 430 and the luminaire 402. Power can also be conserved byselecting a smaller number of nodes 430, or selecting nodes 430 that donot have previous commitments to perform computational tasks.

FIG. 4B illustrates a diagram 412 of the luminaire network 410 where thetask data 406 has been distributed to certain nodes 430 for processing.The darkest node can be the luminaire 402, which originally included thetask data 406, as described with respect to FIG. 4A. The next darkestnodes can be tasked luminaires 414, which can store the received taskdata 418 in their respective memory units 416. The received task data418 can include data and/or instructions for performing a computationaltask. The lightest nodes can be pre-committed luminaires 420, which havepre-scheduled task data 424 in their respective memory units 422. Inorder to complete a computational task more efficiently, the luminaire402 can avoid allocating tasks to pre-committed luminaires 420 becausethere may be idle luminaires that are available for taking oncomputational tasks. Once the received task data 418 has been processedby the tasked luminaire 414, the tasked luminaire 414 can transmitprocessed task data 426 back to the luminaire 402. In some embodiments,the processed task data 426 can be transmitted back to the luminaire 402over the same route of nodes 430 through which the received task data418 was transmitted. In other embodiments, a new route for the processedtask data 426 can be determined by the tasked luminaire 414 based onupdated resource data 428 received by tasked luminaire 414 from theluminaire 402, or generated by the tasked luminaire 414. For example, ifa pre-committed luminaire 420 completes its pre-scheduled task beforethe tasked luminaire 414 completes its received task, the pre-committedluminaire 420 may be idle and therefore serve as a suitable node forrouting data back to the luminaire 402. Once the luminaire 402 receivessome or all of the processed task data 426 back from the taskedluminaires 414, the luminaire 402 can generate updated resource data 428in order to be able to allocate future computational tasks to the node430 in an efficient manner.

FIG. 5 illustrates a method 500 for distributing computational tasks toa group of lighting apparatuses in a network of lighting apparatuses,according to some embodiments. The lighting apparatuses can correspondto any of the lighting apparatuses or luminaires discussed herein. Asshown, the method 500 begins at step 502, where the lighting apparatusreceives task schedules from lighting apparatuses in the network oflighting apparatuses. At step 504, the lighting apparatus determines,based on the task schedules, an amount of computational resourcesavailable from the lighting apparatuses. At step 506, the lightingapparatus identifies a subgroup of lighting apparatuses in the networkof lighting apparatuses that have available computational resources. Thelighting apparatus can determine availability of computational resourcesby comparing certain computational metrics to certain thresholds. Forexample, the lighting apparatus can use the task schedules to estimatethe available RAM in each lighting apparatus in the network forperforming the computational task. If the estimated amount of availableRAM for a particular lighting apparatus is above a threshold, theparticular lighting apparatus can be delegated part of the computationtask. If the estimated amount of available RAM for the particularlighting apparatus is below the threshold, the particular lightingapparatus can be left to perform its pre-committed tasks in its taskschedule. In some embodiments, the lighting apparatus can use taskschedules to determine whether lighting apparatuses on the network areperforming a task and how long the task will take complete before takingon another task. In this way, the lighting apparatus can select idlelighting apparatuses to perform certain computational tasks as well asselect certain lighting apparatuses that have shorter tasks. At step508, the lighting apparatus distributes the computational task to thesubgroup of lighting apparatuses in the network of lighting apparatuses.The distribution of computational tasks can include sending datawirelessly between lighting apparatuses using wireless transceiversincorporated into the lighting apparatuses. Another example involves thedistribution of a parallelizable computation task from one lightingapparatus or remote server, say originating resource, to a subgroup oflighting apparatuses. The lighting apparatus determines the optimalamount of adjoining or additional lighting apparatuses to pool in anddistribute the tasks over. This can be based on network constraints ondistributing the loads, topological constraints, nearness of thelighting apparatus to the point of delivering the computation results,current computation tasks on individual lighting apparatus, historicalutilization of the individual lighting resources, non-criticality of thelighting apparatus amongst others. In some embodiments, a subgroup oflighting apparatuses can be isolated for security purposes in order toprevent tampering of data stored by the lighting apparatuses. Isolationof the subgroup can be performed by temporarily closing communicationsto the subgroup, encrypting data managed by the subgroup using a keystored by at least one of the lighting apparatuses in the subgroup,and/or any other method for isolating a device on a network.

FIG. 6 illustrates a method 600 for distributing and receiving segmenteddata to and from a network of lighting apparatuses for processing,according to some embodiments. As shown, the method 600 begins at step602, where the lighting apparatus receives a sensor signal from a sensorconnected to the lighting apparatus. The sensor can include any of thesensors discussed herein and be directly or indirectly connected to thelighting apparatus. At step 604, the lighting apparatus parses thesignal into segmented data for distribution over a communicationschannel of the lighting apparatus. At step 606, the lighting apparatusdistributes the segmented data to other lighting apparatuses in anetwork of lighting apparatuses. Parsing the signal into segmented datacan include converting the signal into data that is divided intosegments. Parsing the signal data can also include filtering the signalto remove unwanted background data or noise, and dividing the filtereddata into segments for analysis by the lighting apparatuses in thenetwork. It should be noted that in some embodiments the parsing and/orfiltering can be performed by multiple lighting apparatuses in thenetwork. Moreover, the lighting apparatuses that perform the parsing canbe different than the lighting apparatuses that perform the processingon the segmented data. In this way, the network of lighting apparatusescan more equally share the tasks of parsing and processing the segmenteddata. At step 608, the lighting apparatus receives, from the otherlighting apparatuses, processed data prepared by the other lightingapparatuses.

FIG. 7 illustrates a method 700 for optimizing computational resourcesin a network of lighting devices, according to some embodiments. Asshown, the method 700 begins at step 702, wherein a computational taskis localized at a reference lighting apparatus. Localizing thecomputational task can include receiving, at the reference lightingapparatus, data corresponding to a computational task, or the lightingapparatus originating the computational task. At step 704, the referencelighting apparatus identifies a lighting apparatus in a network oflighting apparatuses. At step 706, the reference lighting apparatusdetermines whether the lighting apparatus has available computationalresources. If, at step 706, the reference lighting apparatus determinesthat the identified lighting apparatus meets certain criteria, then themethod 700 proceeds to step 710. These criteria can include but is notlimited to: having currently available computational resources, futureavailable computational resources, topological advantages over others,bandwidth transmission advantages over others, redundancy, nearest topoint of load delivery, and other individual lighting or computationalcharacteristics. Otherwise, the method 700 proceeds to step 708 wherethe identified lighting apparatus is bypassed and a different lightingapparatus is identified at step 704. At step 710, the reference lightingapparatus determines whether communication with the lighting apparatusexceed a communication threshold. The communication threshold can bebased on an available bit rate for transmitting signals between thelighting apparatuses, available bandwidth between the lightingapparatuses, or an amount of time that the communication will take toreach its destination. If, at step 708, the reference lighting apparatusdetermines that the communication with the identified lighting apparatusdoes not exceed a communication threshold, or otherwise does notmaximize or does not fit in optimal subset of apparatuses whichmaximize, the above mentioned criteria, then the method 700 proceeds tostep 708. Otherwise, the method 700 proceeds to step 712. At step 712,the reference lighting apparatus delegates part of a computational taskto the identified lighting apparatus.

At step 714, the reference lighting apparatus determines whether arethere remaining computational task parts to be delegated. If, at step714, the reference lighting apparatus determines that are thereremaining computational task parts to be delegated, then the method 700proceeds to step 708. Otherwise, at step 716, the reference lightingapparatus can optionally designate the computational task as completed.The completion of the computational task can be communicated to theother lighting apparatuses in the network, other local devices, and/or aremote server. For example, if the computational task was delegated tothe reference lighting apparatus from another device, the referencelighting apparatus can transmit a signal to the other device indicatingthat the computational task has been completed. The reference lightingapparatus can also send any data derived from completing thecomputational task, for example, a compressed image or a summary ofdata.

FIG. 8 illustrates a method 800 for delegating computational tasks tolighting apparatuses in a network of lighting apparatuses according towhether the lighting apparatuses are located in an occupied room. Themethod 800 can be performed by any apparatus, computing device, and/orlighting apparatus discussed herein. The method 800 can begin at step802, where a computational task is localized to a reference lightingapparatus. At step 804, the reference lighting apparatus identifies alighting apparatus in a network of lighting apparatuses. At step 806,the reference lighting apparatus can determine occupancy of a roomilluminated by the identified lighting apparatus. Occupancy can refer towhether one or more persons of present inside of the room, and/or acount of the number of persons within the room. The reference lightingapparatus can determine occupancy using room schedules accessible to thereference lighting apparatus. For example, the reference lightingapparatus can download meeting schedules from devices located within therooms of the building and/or from a personal computing device of someonewho is participating in a meeting. In some embodiments, the referencelighting apparatus can determine occupancy by determining occupancypatterns of rooms in the building. Occupancy patterns can be determinedusing the various lighting apparatuses in the network of lightingapparatuses. The lighting apparatuses can be located in different roomsof the building and track when people are moving in and out of differentrooms. By collecting data about the movement of people within thebuilding and the different rooms, the lighting apparatuses can use thedata to determine occupancy patterns for the different rooms as well asthe entire building. At step 808, a determination is made as to whetherthe room is occupied during a time for performing the computationaltask. The time for performing the computational task can be the presenttime or a scheduled time in the future. If the room is occupied duringthe time for performing the computational task, the identified lightingapparatus can be bypassed at step 810, and a different lightingapparatus can be identified at step 804. If the room is not occupiedduring the time for performing the computational task, then, at step812, at least part of the computational task can be delegated to theidentified lighting apparatus.

While several inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

It should also be understood that, unless clearly indicated to thecontrary, in any methods claimed herein that include more than one stepor act, the order of the steps or acts of the method is not necessarilylimited to the order in which the steps or acts of the method arerecited.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03. It should be understoodthat certain expressions and reference signs used in the claims pursuantto Rule 6.2(b) of the Patent Cooperation Treaty (“PCT”) do not limit thescope.

The invention claimed is:
 1. A method for performing a computationaltask using a network of lighting apparatuses, the method comprisingsteps that include: by a lighting apparatus in the network of lightingapparatuses: receiving task schedules from lighting apparatuses in thenetwork of lighting apparatuses; determining, based on the taskschedules, an amount of computational resources available from thelighting apparatuses; determining an occupancy pattern of a roomilluminated by at least one lighting apparatus in the network oflighting apparatuses; identifying a subgroup of lighting apparatuses nthe network of lighting apparatuses that have available computationalresources, wherein identifying the subgroup of lighting apparatuses isat least partially based on the occupancy pattern of the room; anddistributing the computational task to the subgroup of lightingapparatuses in the network of lighting apparatuses.
 2. The method ofclaim 1, wherein the occupancy pattern is determined using one or morecameras installed in one or more lighting apparatuses in the network oflighting apparatuses.
 3. The method of claim 1, wherein the stepsfurther include: receiving energy usage data from the lightingapparatuses in the network of lighting apparatuses, wherein distributingthe computational task to the subgroup is at least partially based onthe received energy usage data.
 4. The method of claim 1, wherein thesteps further include: determining a connectivity requirement fordistributing the computational task to the subgroup of lightingapparatuses in the network of lighting apparatuses.
 5. The method ofclaim 4, wherein the steps further include: parsing the computationaltask into subtasks at least partially based on the connectivityrequirement for distributing the computational task, whereindistributing the computational task includes distributing the subtasks;determining that a fault has occurred at a subgroup lighting apparatus;and redistributing a subtask from the subgroup lighting apparatus to adifferent subgroup lighting apparatus.
 6. A non-transitorycomputer-readable medium configured to store instructions that whenexecuted by one or more processors of a computing device, cause thecomputing device to perform steps that include: receiving, from alighting apparatus in a network of connected lighting apparatuses,charge data that indicates a battery charge level of the lightingapparatus; identifying, at least partially based on the charge datareceived from the lighting apparatus, a subgroup of lighting apparatusesto perform a computational task; parsing the computational task intosubtasks to be performed by the subgroup of individual lightingapparatuses; and transmitting, to the subgroup of individual lightingapparatuses, data corresponding to the subtasks.
 7. The non-transitorycomputer-readable medium of claim 6, wherein the steps further include:determining that the battery charge level is within a low thresholdstored by the computing device, wherein the lighting apparatus isexcluded from the subgroup based on the battery charge level beingwithin the low threshold.
 8. The non-transitory computer-readable mediumof claim 7, wherein the steps further include: causing at least onelighting apparatus in the network of lighting apparatuses to sharebattery charge data with the lighting apparatus excluded from thesubgroup.
 9. The non-transitory computer-readable medium of claim 6,wherein the steps further include: determining that the computationaltask is complete; receiving an updated battery charge level from thelighting apparatus; and determining an amount of charge consumed by thelighting apparatus as a result of performing the task.
 10. Thenon-transitory computer-readable medium of claim 6, wherein the stepsfurther include: collecting environmental data using a sensor of thecomputing device; parsing the collected environmental data into subsetsof collected data; and transmitting, to the individual lightingapparatuses, the subsets of collected data with the data correspondingto the subtasks.
 11. The non-transitory computer-readable medium ofclaim 10, wherein the computational task includes determining whethermotion has occurred in an environment illuminated by the network ofconnected lighting apparatuses.